Literature DB >> 21781376

A practical approach to the early identification of antidepressant medication non-responders.

J Li1, A Y C Kuk1, A J Rush2.   

Abstract

BACKGROUND: The aim of the present study was to determine whether a combination of baseline features and early post-baseline depressive symptom changes have clinical value in predicting out-patient non-response in depressed out-patients after 8 weeks of medication treatment.
METHOD: We analysed data from the Combining Medications to Enhance Depression Outcomes study for 447 participants with complete 16-item Quick Inventory of Depressive Symptomatology - Self-Report (QIDS-SR16) ratings at baseline and at treatment weeks 2, 4 and 8. We used a multi-time point, recursive subsetting approach that included baseline features and changes in QIDS-SR16 scores from baseline to weeks 2 and 4, to identify non-responders (<50% reduction in QIDS-SR16) at week 8 with a pre-specified accuracy level.
RESULTS: Pretreatment clinical features alone were not clinically useful predictors of non-response after 8 weeks of treatment. Baseline to week 2 symptom change identified 48 non-responders (of which 36 were true non-responders). This approach gave a clinically meaningful negative predictive value of 0.75. Symptom change from baseline to week 4 identified 79 non-responders (of which 60 were true non-responders), achieving the same accuracy. Symptom change at both weeks 2 and 4 identified 87 participants (almost 20% of the sample) as non-responders with the same accuracy. More participants with chronic than non-chronic index episodes could be accurately identified by week 4.
CONCLUSIONS: Specific baseline clinical features combined with symptom changes by weeks 2-4 can provide clinically actionable results, enhancing the efficiency of care by personalizing the treatment of depression.

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Year:  2011        PMID: 21781376     DOI: 10.1017/S0033291711001280

Source DB:  PubMed          Journal:  Psychol Med        ISSN: 0033-2917            Impact factor:   7.723


  6 in total

1.  Targeting treatments for depression: what can our patients tell us?

Authors:  A John Rush
Journal:  Epidemiol Psychiatr Sci       Date:  2016-04-05       Impact factor: 6.892

Review 2.  Personalized medicine in major depressive disorder -- opportunities and pitfalls.

Authors:  Diane B Miller; James P O'Callaghan
Journal:  Metabolism       Date:  2012-09-26       Impact factor: 8.694

3.  Is the Ultimate Treatment Response Predictable with Early Response in Major Depressive Episode?

Authors:  Aslı Çiftçi; Halis Ulaş; Ahmet Topuzoğlu; Zeliha Tunca
Journal:  Noro Psikiyatr Ars       Date:  2016-09-01       Impact factor: 1.339

Review 4.  An update on antidepressant use in bipolar depression.

Authors:  Michelle M Sidor; Glenda M MacQueen
Journal:  Curr Psychiatry Rep       Date:  2012-12       Impact factor: 5.285

5.  Toward an online cognitive and emotional battery to predict treatment remission in depression.

Authors:  Evian Gordon; A John Rush; Donna M Palmer; Taylor A Braund; William Rekshan
Journal:  Neuropsychiatr Dis Treat       Date:  2015-02-26       Impact factor: 2.570

6.  Accurately identifying patients who are excellent candidates or unsuitable for a medication: a novel approach.

Authors:  A John Rush; Madhukar H Trivedi; Charles South; Thomas J Carmody; Manish K Jha
Journal:  Neuropsychiatr Dis Treat       Date:  2017-12-15       Impact factor: 2.570

  6 in total

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